Transcription factor activity inference from scRNA-seq
25 April 2023
Package
decoupleR 2.6.0
scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring transcription factor (TF) activities from prior knowledge.
In this notebook we showcase how to use decoupleR for transcription factor activity
inference with a down-sampled PBMCs 10X data-set. The data consists of 160
PBMCs from a Healthy Donor. The original data is freely available from 10x Genomics
here
from this webpage.
1 Loading packages
First, we need to load the relevant packages, Seurat to handle scRNA-seq data
and decoupleR to use statistical methods.
## We load the required packages
library(Seurat)
library(decoupleR)
# Only needed for data handling and plotting
library(dplyr)
library(tibble)
library(tidyr)
library(patchwork)
library(ggplot2)
library(pheatmap)2 Loading the data-set
Here we used a down-sampled version of the data used in the Seurat
vignette.
We can open the data like this:
inputs_dir <- system.file("extdata", package = "decoupleR")
data <- readRDS(file.path(inputs_dir, "sc_data.rds"))We can observe that we have different cell types:
DimPlot(data, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
3 CollecTRI network
CollecTRI is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous DoRothEA network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).
For this example we will use the human version (mouse and rat are also
available). We can use decoupleR to retrieve it from OmniPath. The argument
split_complexes keeps complexes or splits them into subunits, by default we
recommend to keep complexes together.
net <- get_collectri(organism='human', split_complexes=FALSE)
net
#> # A tibble: 45,398 × 3
#> source target mor
#> <chr> <chr> <dbl>
#> 1 MYC TERT 1
#> 2 SPI1 BGLAP 1
#> 3 SMAD3 ADAM2 1
#> 4 SMAD3 JUN 1
#> 5 SMAD4 ADAM2 1
#> 6 SMAD4 JUN 1
#> 7 STAT5A IL2 1
#> 8 STAT5B IL2 1
#> 9 RELA FAS 1
#> 10 WT1 NR0B1 1
#> # ℹ 45,388 more rows4 Activity inference with Weighted Mean
To infer activities we will run the Weighted Mean method (wmean). It infers
regulator activities by first multiplying each target feature by its associated
weight which then are summed to an enrichment score wmean. Furthermore,
permutations of random target features can be performed to obtain a null
distribution that can be used to compute a z-score norm_wmean, or a corrected
estimate corr_wmean by multiplying wmean by the minus log10 of the obtained
empirical p-value.
In this example we use wmean but we could have used any other.
To see what methods are available use show_methods().
To run decoupleR methods, we need an input matrix (mat), an input prior
knowledge network/resource (net), and the name of the columns of net that we
want to use.
# Extract the normalized log-transformed counts
mat <- as.matrix(data@assays$RNA@data)
# Run wmean
acts <- run_wmean(mat=mat, net=net, .source='source', .target='target',
.mor='mor', times = 100, minsize = 5)
acts
#> # A tibble: 249,600 × 5
#> statistic source condition score p_value
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 corr_wmean ABL1 AAACATACAACCAC-1 1.16 0.02
#> 2 corr_wmean ABL1 AAACGCTGTTTCTG-1 0.121 0.38
#> 3 corr_wmean ABL1 AACCTTTGGACGGA-1 1.24 0.02
#> 4 corr_wmean ABL1 AACGCCCTCGTACA-1 0.533 0.1
#> 5 corr_wmean ABL1 AACGTCGAGTATCG-1 0.468 0.12
#> 6 corr_wmean ABL1 AACTCACTCAAGCT-1 0.563 0.08
#> 7 corr_wmean ABL1 AAGATGGAAAACAG-1 0.269 0.14
#> 8 corr_wmean ABL1 AAGATTACCGCCTT-1 1.17 0.02
#> 9 corr_wmean ABL1 AAGCCATGAACTGC-1 0.397 0.16
#> 10 corr_wmean ABL1 AAGGTCTGCAGATC-1 0 0.42
#> # ℹ 249,590 more rows5 Visualization
From the obtained results, we will select the norm_wmean activities and store
them in our object as a new assay called tfswmean:
# Extract norm_wmean and store it in tfswmean in pbmc
data[['tfswmean']] <- acts %>%
filter(statistic == 'norm_wmean') %>%
pivot_wider(id_cols = 'source', names_from = 'condition',
values_from = 'score') %>%
column_to_rownames('source') %>%
Seurat::CreateAssayObject(.)
# Change assay
DefaultAssay(object = data) <- "tfswmean"
# Scale the data
data <- ScaleData(data)
data@assays$tfswmean@data <- data@assays$tfswmean@scale.dataThis new assay can be used to plot activities. Here we observe the activity inferred for PAX5 across cells, which it is particulary active in B cells. Interestingly, PAX5 is a known TF crucial for B cell identity and function. The inference of activities from “foot-prints” of target genes is more informative than just looking at the molecular readouts of a given TF, as an example here is the gene expression of PAX5, which is not very informative by itself:
p1 <- DimPlot(data, reduction = "umap", label = TRUE, pt.size = 0.5) +
NoLegend() + ggtitle('Cell types')
p2 <- (FeaturePlot(data, features = c("PAX5")) &
scale_colour_gradient2(low = 'blue', mid = 'white', high = 'red')) +
ggtitle('PAX5 activity')
DefaultAssay(object = data) <- "RNA"
p3 <- FeaturePlot(data, features = c("PAX5")) + ggtitle('PAX5 expression')
DefaultAssay(object = data) <- "tfswmean"
p1 | p2 | p3
6 Exploration
We can also see what is the mean activity per group of the top 20 more variable TFs:
n_tfs <- 25
# Extract activities from object as a long dataframe
df <- t(as.matrix(data@assays$tfswmean@data)) %>%
as.data.frame() %>%
mutate(cluster = Idents(data)) %>%
pivot_longer(cols = -cluster, names_to = "source", values_to = "score") %>%
group_by(cluster, source) %>%
summarise(mean = mean(score))
# Get top tfs with more variable means across clusters
tfs <- df %>%
group_by(source) %>%
summarise(std = sd(mean)) %>%
arrange(-abs(std)) %>%
head(n_tfs) %>%
pull(source)
# Subset long data frame to top tfs and transform to wide matrix
top_acts_mat <- df %>%
filter(source %in% tfs) %>%
pivot_wider(id_cols = 'cluster', names_from = 'source',
values_from = 'mean') %>%
column_to_rownames('cluster') %>%
as.matrix()
# Choose color palette
palette_length = 100
my_color = colorRampPalette(c("Darkblue", "white","red"))(palette_length)
my_breaks <- c(seq(-3, 0, length.out=ceiling(palette_length/2) + 1),
seq(0.05, 3, length.out=floor(palette_length/2)))
# Plot
pheatmap(top_acts_mat, border_color = NA, color=my_color, breaks = my_breaks) 
Here we can observe other known marker TFs appearing, EBF1 for B cells RFX5 for the myeloid lineage and EOMES for the lymphoid.
7 Session information
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